Introduction
Multiple myeloma (MM), a malignancy of plasma cells that accumulate in the bone marrow, accounts for an estimated 1.8% of all new cancer cases and 19% of all newly diagnosed hematologic malignancies in the United States (US) [ 1–3 ]. The 5-year survival rate for patients with MM between 2014 and 2020 was 61.1% overall [ 1 ]. The American Cancer Society estimates that about 35,780 new cases will be diagnosed, and approximately 12,540 people will die from MM in 2024 [ 4 ]. While there is currently no cure for MM, national guidelines recommend initial treatment with a combination of immunomodulatory drugs (IMiD) (e.g., thalidomide, lenalidomide), proteasome inhibitors (PI) (e.g., bortezomib, carfilzomib, and ixazomib), and anti-CD38 monoclonal antibodies (MAb) (e.g., daratumumab, daratumumab and hyaluronidase, and isatuximab); followed by high-dose chemotherapy and/or hematopoietic stem cell transplant in eligible patients [ 5–10 ]. Patients previously treated with PI, IMiD, and anti-CD38 MAb drugs are referred to as triple class exposed (TCE) and are considered a heavily pre-treated group. For patients with RRMM who are experiencing clinical relapse, national guidelines recommend that the next line of therapy (LOT) include three agents from drug classes that the patient has not been exposed to previously, or at least not within the last 6 months [ 11 ]. While there have been considerable advances in treatment options, there is currently no cure for MM disease. As a result, patients eventually relapse and require novel, more effective treatments.
B-cell maturation antigen (BCMA)-targeted chimeric antigen receptor T-cell (CAR-T) therapies (e.g., idecabtagene vicleucel – approved 2021; ciltacabtagene autoleucel – approved 2022) and bispecific T-cell engaging antibodies (e.g., teclistamab – approved 2022; elranatamab – approved 2023), have emerged as novel options for heavily pre-treated patients with RRMM [ 12–16 ]. Additionally, talquetamab – approved August 2023, is a bispecific antibody which redirects T-cells by simultaneously binding to CD3 receptors and G protein-coupled receptor class C group 5 member D (GPRC5D) expressed by MM cells [ 17 ]. These novel therapies are approved for RRMM patients who are TCE and have received ≥4 prior LOTs, although recently, idecabtagene vicleucel has been approved for ≥2 prior LOTs, and ciltacabtagene autoleucel has been approved for patients treated with ≥1 prior LOT, including a PI and an IMiD agent, and who are refractory to lenalidomide [ 12–16 , 18 ]. Despite the recent approvals and availability of BCMA-targeted therapies, there is limited real-world evidence on the burden of illness among patients who are eligible but have not yet received these novel treatments. Therefore, this study aims to examine the disease burden – including comorbidities, MM-related conditions, healthcare resource utilization (HCRU), and its associated costs – among heavily pre-treated RRMM TCE patients who are eligible for BCMA-targeted CAR-T and bispecific therapies but have not yet received these novel treatments per label.
Materials and methods
Study design and data sources
This retrospective, observational cohort study identified eligible patients from Carelon Research’s Healthcare Integrated Research Database (HIRD®). The Healthcare Integrated Research Database (HIRD®) is a proprietary healthcare database curated and maintained by Carelon Research. The HIRD includes members whose data is eligible for research based upon certain data use permissions. Medical claims, pharmacy claims, oncology, and sociodemographic data are all integrated into the HIRD® and were accessed as a limited data set in compliance with the Health Insurance Portability and Accountability Act (HIPAA). The study data did not involve interactions or interviews with study subjects. Since all data were fully de-identified, the current study did not qualify as human subject research as defined in 45 CFR 46.102(f)(2) and was exempt from institutional review board approval.
Medical and pharmacy claims
The HIRD® contains medical and pharmacy claims integrated across data sources (i.e., professional, facility, and outpatient pharmacy claims and enrollment information) for approximately 90 million members [ 19 ]. Data are obtained from health plans across the US and represent members in each of the 50 states [ 20 ]. The database represents fully adjudicated claims information from a commercially insured and Medicare Advantage population and includes health maintenance, point of service, preferred provider organizations, and indemnity health plan types [ 19 , 20 ].
Oncology data
Oncology data from the health plans’ Cancer Care Quality Program (CCQP), including Eastern Cooperative Oncology Group (ECOG) performance status data available for a subset of patients, were integrated with medical claims data into the HIRD [ 21 ].
Sociodemographic data
Sociodemographic (age, race/ethnicity) and social determinants of health (SDoH) data were obtained from health plan enrollment records, administrative claims, and the American Community Survey (ACS) and were linked to patient data available in the HIRD® using nine-digit zip codes [ 22 , 23 ]. SDoH are area-level data comprised of education, income, living condition (home value, ownership type, crowding), and employment which were used to calculate the composite Socio-economic Status (SES) Index Score, which ranges from 0 (worst SES) to 100 (best SES) [ 24 ].
Mortality data
Mortality was identified in the HIRD® through a combination of inpatient discharge status, disenrollment reasons, third-party obituary data, utilization management data, Center for Medicare and Medicaid Services records, and the Death Master File sourced from the US Social Security Administration. Mortality data from these sources were combined to create a composite mortality variable for research purposes, with a method previously validated among a sample of patients with advanced cancer [ 25 , 26 ].
Study timeline and population
Patients were identified between 1 January 2016, and 30 September 2022 (patient identification period) requiring 12 months prior to the index date (baseline period), and at least 6 months of follow-up. The lookback period went as early as 2006 to capture treatment history. The index date was defined as the start date of the earliest LOT (a fifth or later LOT) after the patient became TCE. The follow-up period included the time from the index date to the end of continuous health plan enrollment, end of study period, or death, whichever came first; those who died within the first 6 months during follow-up were included in the analysis ( Figure 1 ). Eligible patients met the following inclusion criteria: ≥2 medical claims for MM diagnosis (ICD-9-CM = 203.00, 203.01, 203.02; ICD-10-CM=C90.00, C90.01, C90.02) ≥30 days apart during the study period; previously treated (TCE) and have initiated a fifth or later LOT during the patient identification period; ≥18 years of age on the index date; ≥12 months of continuous health plan enrollment with both medical and pharmacy coverage before the index date and for ≥6 months after the index date. Patients were required to be eligible for BCMA-targeted therapies (TCE and ≥4 LOT) but have not yet received them as of the index date. Patients with a prior diagnosis of MM or prior use of MM medications in the ≥12 months before the first MM diagnosis during the patient identification period were excluded. Figure 1. Study periods. Abbreviations: LOT, Line of therapy; 5 L, fifth line; TCE, Triple class exposed; MM, Multiple Myeloma; HIRD, Healthcare Integrated Research Database. Source=authors’ own data.
LOT algorithm
A claims-based algorithm was used for regimen identification and LOT assignment.
A regimen was formed by one or several recommended medications for MM identified from medical and pharmacy claims during the first 60 days following regimen initiation. A patient may receive multiple regimens sequentially throughout their course of treatment. The LOT start date was defined as the first claim date for an MM treatment observed during the study period. Each regimen ended on either the discontinuation date, the date of treatment change (including augmentation and switching), or end of follow-up, whichever occurred first. Treatment discontinuation was defined as a gap in MM treatment of more than 90 days after running out of medication (days’ supply minus 1 day). The discontinuation date was defined as the service date of last dispensing before the treatment discontinuation gap. The treatment change date was defined as the next LOT initiation date. Changes in any MM agent more than 90 days after LOT start date were considered a new LOT. Discontinuation of a regimen was defined as discontinuation of all medications included in the regimen.
Study variables and outcomes
Comprehensive identification of comorbid conditions in patients with RRMM is a requisite step toward optimal care. At baseline, the most common comorbidities and MM-related conditions were examined using a combination of modified Quan Charlson Comorbidity (QCI) score, ICD-10-CM diagnoses, and the Clinical Classifications Software Refined (CCSR) categories [ 27–29 ]. The modified QCI was calculated without MM and bone metastasis because bone involvement is innate to the MM disease process.
Study outcomes were clinical burden of disease, initiation of new LOT, time to next treatment (TTNT), mortality, time to death, HCRU, and healthcare costs. Treatment failure was defined as initiation of new MM therapy or death within 6-months of follow-up. The incidence rate of new treatment initiation or death was reported as the number of patients who had the event per 100 person-years. Patients who neither initiated new treatment nor had a death date were followed until the end of the study period.
All-cause HCRU and costs were calculated during 12-month baseline and follow-up (≥6 months) periods for patients with at least one claim for inpatient hospitalizations, emergency department (ED) visits, outpatient visits, skilled nursing facility (SNF), and prescription drug fills. All-cause total medical costs are the sum of costs associated with hospitalizations, ED visits, outpatient visits, and SNF. All-cause total healthcare costs were the sum of total medical and pharmacy costs. Due to variable follow-up time, HCRU and costs were calculated per patient per month (PPPM). All costs were adjusted to 2022 dollars based on updated Consumer Price Index from the US Bureau of Labor Statistics [ 30 ].
Statistical analysis
Patient demographics, clinical characteristics, HCRU, and healthcare costs were reported by descriptive statistics. Frequencies, percentages, and 95% confidence intervals were reported for categorical variables. Relevant measures of centrality and variance, such as mean, median, standard deviation, interquartile range, and 95% confidence intervals were used to report continuous variables. All statistical analyses were performed using Instant Health Data (IHD®) Analytics platform (Panalgo, Boston, MA) and SAS Enterprise Guide 7.12 (SAS Institute, Cary, NC).
Results
Patient population
The final study sample consisted of 228 patients who initiated a new LOT (fifth or later LOT) after becoming eligible for but had not started a BCMA-targeted therapy ( Figure 2 ). The median (IQR) age was 64 (58–72) years, and 17.1% were ≥75 years on the index date ( Table 1 ). Male patients represented just over half of the population (52.6%), and 68.7% were White, who were non-Hispanic/Latino. The geographic distribution of the population showed a higher proportion of patients residing in the western region (34.1%) of the US. Most patients (66.7%) had preferred provider organization (PPO) health plan type, and 85.1% of the overall study sample were commercially insured ( Table 1 ). SES Index Score category was available for 94.7% of the sample, and most patients were in the top 50% of the SES index (category 3: 24.1%; category 4: 47.7%). SES individual components at the neighborhood level indicated low rates of household crowding (2.7%), poverty (9.8%), and individuals with less than a 12th-grade education (6.6%). ( Table 1 ). Figure 2. Patient selection and attrition. Abbreviations: MM, Multiple Myeloma; TCE, Triple class exposed; CAR-T, chimeric antigen receptor T-cell; BCMA B-cell maturation antigen. Source=authors’ own data. Table 1. Baseline patient characteristics. Patient demographics Results Number of patients, n 228 Age in years, median (IQR) 64 (58–72) Age categories, n (%) ≥18 to <65 116 (50.9%) ≥65 to <75 73 (32.0%) ≥75 39 (17.1%) Male sex, n (%) 120 (52.6%) United States geographic region of residence, n (%) Midwest 49 (22.2%) Northeast 45 (20.2%) South 53 (23.8%) West 76 (34.1%) Health plan type, n (%) Health maintenance organization (HMO) 45 (19.7%) Preferred provider organization (PPO) 152 (66.7%) Consumer-directed health plan (CDHP) 30 (13.2%) Insurance type, n (%) Commercial health plan 194 (85.1%) Medicare Advantage health plan 34 (14.9%) Member-level race/ethnicity a , among patients with available data, n (%) 182 White, not Hispanic or Latino 125 (68.7%) Black or African American, not Hispanic or Latino 28 (15.4%) Hispanic or Latino of any race 18 (9.9%) Asian, not Hispanic or Latino 7 (3.8%) Other race, including Indian or Alaska Native, not Hispanic or Latino ≤5 Neighborhood-level (SES) Index Score category, n (%) 1 (bottom 25%) 16 (7.4%) 2 45 (20.8%) 3 52 (24.1%) 4 (top 25%) 103 (47.7%) Neighborhood-level SES individual components, percentage with: Households with ≥1 person per room (crowding), mean (SD) 2.7% (4.9%) Median (IQR) home value of owner-occupied homes, USD ($) 317,200 (178,250–577,350) Persons below the federally defined poverty line, mean (SD) 9.8% (10.6%) Median (IQR) household income in 2019 inflation-adjusted USD ($) 95,344 (68,864–130,754) Persons aged ≥25 years with ≥4 years of college, mean (SD) 40.5% (22.5%) Persons aged ≥25 years with <12 th grade education, mean (SD) 6.6% (7.3%) Persons aged ≥16 years who are unemployed, mean (SD) 5.6% (4.9%) Abbreviations: IQR, interquartile range; SD, standard deviation; SES, socioeconomic status; USD, United States dollars. a Member-level race/ethnicity, and SES category percentages have been calculated for the number of patients with available data as the denominator. Cells with n ≤ 5 are not specified to protect member privacy.
Table 1.
Baseline patient characteristics.
Abbreviations: IQR, interquartile range; SD, standard deviation; SES, socioeconomic status; USD, United States dollars. a Member-level race/ethnicity, and SES category percentages have been calculated for the number of patients with available data as the denominator. Cells with n ≤ 5 are not specified to protect member privacy.
| Patient demographics | Results |
| Number of patients, n | 228 |
| Age in years, median (IQR) | 64 (58–72) |
| Age categories, n (%) | |
| ≥18 to <65 | 116 (50.9%) |
| ≥65 to <75 | 73 (32.0%) |
| ≥75 | 39 (17.1%) |
| Male sex, n (%) | 120 (52.6%) |
| United States geographic region of residence, n (%) | |
| Midwest | 49 (22.2%) |
| Northeast | 45 (20.2%) |
| South | 53 (23.8%) |
| West | 76 (34.1%) |
| Health plan type, n (%) | |
| Health maintenance organization (HMO) | 45 (19.7%) |
| Preferred provider organization (PPO) | 152 (66.7%) |
| Consumer-directed health plan (CDHP) | 30 (13.2%) |
| Insurance type, n (%) | |
| Commercial health plan | 194 (85.1%) |
| Medicare Advantage health plan | 34 (14.9%) |
| Member-level race/ethnicity a , among patients with available data, n (%) | 182 |
| White, not Hispanic or Latino | 125 (68.7%) |
| Black or African American, not Hispanic or Latino | 28 (15.4%) |
| Hispanic or Latino of any race | 18 (9.9%) |
| Asian, not Hispanic or Latino | 7 (3.8%) |
| Other race, including Indian or Alaska Native, not Hispanic or Latino | ≤5 |
| Neighborhood-level (SES) Index Score category, n (%) | |
| 1 (bottom 25%) | 16 (7.4%) |
| 2 | 45 (20.8%) |
| 3 | 52 (24.1%) |
| 4 (top 25%) | 103 (47.7%) |
| Neighborhood-level SES individual components, percentage with: | |
| Households with ≥1 person per room (crowding), mean (SD) | 2.7% (4.9%) |
| Median (IQR) home value of owner-occupied homes, USD ($) | 317,200 (178,250–577,350) |
| Persons below the federally defined poverty line, mean (SD) | 9.8% (10.6%) |
| Median (IQR) household income in 2019 inflation-adjusted USD ($) | 95,344 (68,864–130,754) |
| Persons aged ≥25 years with ≥4 years of college, mean (SD) | 40.5% (22.5%) |
| Persons aged ≥25 years with <12 th grade education, mean (SD) | 6.6% (7.3%) |
| Persons aged ≥16 years who are unemployed, mean (SD) | 5.6% (4.9%) |
Clinical burden of disease
Most patients (85.2%) had an ECOG performance score of 0–1, while 14.8% had a score of ≥2. The mean (SD) QCI calculated without MM and bone metastasis was 2.8 (3.0), and most patients had a categorical QCI score of 2 (22.4%) or 3+ (37.3%). The proportion of patients with selected relevant individual QCI comorbidities, including renal disease, diabetes without chronic complication, congestive heart failure, chronic pulmonary disease, diabetes with chronic complication, peripheral vascular disease, cerebrovascular disease, mild liver disease, and myocardial infarction, are presented in Table 2 . The most common comorbidities and MM-related conditions from the CCSR categories were anemias (65.4%), hypertension (64.9%), neutropenia (36.8%), renal dysfunction (36.8%), peripheral neuropathy (36.4%), bone diseases including lytic bone lesions (32.9%), and hypogammaglobulinemia (20.6%) ( Table 2 ). At baseline, most patients (82.5%) were characterized as robust or prefrail (15.8%). Table 2. Baseline clinical characteristics, and comorbidities. Characteristic Result Number of unique patients, n 228 ECOG performance status a , among patients with available data, n (%) 142 0–1 121 (85.2%) ≥2 21 (14.8%) Modified QCI (without MM and bone metastasis b ), Mean (SD) 2.8 (3.0) Modified QCI categories (without MM and bone metastasis b ), n (%) 0 58 (25.4%) 1 34 (14.9%) 2 51 (22.4%) 3+ 85 (37.3%) Modified QCI individual comorbidities, n (%) Renal disease 71 (31.1%) Diabetes without chronic complication 57 (25.0%) Congestive heart failure 50 (21.9%) Chronic pulmonary disease 47 (20.6%) Diabetes with chronic complication 29 (12.7%) Peripheral vascular disease 24 (10.5%) Cerebrovascular disease 19 (8.3%) Mild liver disease 16 (7.0%) Myocardial infarction 14 (6.1%) Hemiplegia or paraplegia ≤5 Dementia ≤5 Rheumatologic disease ≤5 Peptic ulcer disease ≤5 MM-related conditions and complications, n (%) Anemias (including pancytopenia and aplastic anemia) 149 (65.4%) Hypertension 148 (64.9%) Neutropenia (including febrile neutropenia and chemo-induced neutropenia) 84 (36.8%) Renal dysfunction c 84 (36.8%) Peripheral neuropathy 83 (36.4%) Existing bone diseases including lytic bone lesions 75 (32.9%) Hypogammaglobulinemia 47 (20.6%) Other blood cancers 22 (9.6%) Osteoporosis 22 (9.6%) Hypercalcemia 20 (8.8%) Metastatic solid tumor excluding bone metastasis 16 (7.0%) Amyloidosis 13 (5.7%) Extramedullary plasmacytomas 10 (4.4%) Hyperuricemia 6 (2.6%) Baseline claims-based frailty index(CFI) [ 49 ] group, n (%) 01. Robust (<0.15) 188 (82.5%) 02. Prefrail (0.15–0.24) 36 (15.8%) 03, − 04. Frail (Mild to Severe) (0.25–0.35+) ≤5 Abbreviations: ECOG, Eastern Cooperative Oncology Group, IQR, interquartile range; MM, multiple myeloma; SD, standard deviation; QCI, Quan Charlson Comorbidity Index. a ECOG performance category percentages have been calculated with the number of patients with available ECOG data as the denominator. b Bone metastasis identified by ICD 9/10 diagnoses codes were excluded from QCI calculation because bone involvement is integral to MM disease process. c Renal dysfunction includes Chronic Kidney Disease (CKD) stage 1–5, End Stage Renal Disease, unspecified CKD, dialysis, renal failure, and kidney transplant. Cells with n ≤ 5 are not specified to protect member privacy.
Table 2.
Baseline clinical characteristics, and comorbidities.
Abbreviations: ECOG, Eastern Cooperative Oncology Group, IQR, interquartile range; MM, multiple myeloma; SD, standard deviation; QCI, Quan Charlson Comorbidity Index. a ECOG performance category percentages have been calculated with the number of patients with available ECOG data as the denominator. b Bone metastasis identified by ICD 9/10 diagnoses codes were excluded from QCI calculation because bone involvement is integral to MM disease process. c Renal dysfunction includes Chronic Kidney Disease (CKD) stage 1–5, End Stage Renal Disease, unspecified CKD, dialysis, renal failure, and kidney transplant. Cells with n ≤ 5 are not specified to protect member privacy.
| Characteristic | Result |
| Number of unique patients, n | 228 |
| ECOG performance status a , among patients with available data, n (%) | 142 |
| 0–1 | 121 (85.2%) |
| ≥2 | 21 (14.8%) |
| Modified QCI (without MM and bone metastasis b ), Mean (SD) | 2.8 (3.0) |
| Modified QCI categories (without MM and bone metastasis b ), n (%) | |
| 0 | 58 (25.4%) |
| 1 | 34 (14.9%) |
| 2 | 51 (22.4%) |
| 3+ | 85 (37.3%) |
| Modified QCI individual comorbidities, n (%) | |
| Renal disease | 71 (31.1%) |
| Diabetes without chronic complication | 57 (25.0%) |
| Congestive heart failure | 50 (21.9%) |
| Chronic pulmonary disease | 47 (20.6%) |
| Diabetes with chronic complication | 29 (12.7%) |
| Peripheral vascular disease | 24 (10.5%) |
| Cerebrovascular disease | 19 (8.3%) |
| Mild liver disease | 16 (7.0%) |
| Myocardial infarction | 14 (6.1%) |
| Hemiplegia or paraplegia | ≤5 |
| Dementia | ≤5 |
| Rheumatologic disease | ≤5 |
| Peptic ulcer disease | ≤5 |
| MM-related conditions and complications, n (%) | |
| Anemias (including pancytopenia and aplastic anemia) | 149 (65.4%) |
| Hypertension | 148 (64.9%) |
| Neutropenia (including febrile neutropenia and chemo-induced neutropenia) | 84 (36.8%) |
| Renal dysfunction c | 84 (36.8%) |
| Peripheral neuropathy | 83 (36.4%) |
| Existing bone diseases including lytic bone lesions | 75 (32.9%) |
| Hypogammaglobulinemia | 47 (20.6%) |
| Other blood cancers | 22 (9.6%) |
| Osteoporosis | 22 (9.6%) |
| Hypercalcemia | 20 (8.8%) |
| Metastatic solid tumor excluding bone metastasis | 16 (7.0%) |
| Amyloidosis | 13 (5.7%) |
| Extramedullary plasmacytomas | 10 (4.4%) |
| Hyperuricemia | 6 (2.6%) |
| Baseline claims-based frailty index(CFI) [ 49 ] group, n (%) | |
| 01. Robust (<0.15) | 188 (82.5%) |
| 02. Prefrail (0.15–0.24) | 36 (15.8%) |
| 03, − 04. Frail (Mild to Severe) (0.25–0.35+) | ≤5 |
Clinical outcomes
The treatment failure incidence rate during follow-up was 110.9 per 100 person-years. Among the 102 (44.7%) patients who experienced treatment failure, 60 (26.3%) initiated new treatment: the median (IQR) TTNT was 6.0 (3.9–9.0) months, while 54 (23.7%) died within the 6-month follow-up period. The overall mortality rate was 34.9 per 100 person-years.
HCRU
The median (IQR) follow-up duration was 11.8 (5.9–21.7) months. All-cause HCRU measures increased from baseline to follow-up, e.g., the proportion of patients with ≥1 inpatient admission increased from 48.3% at baseline to 71.1% during follow-up ( Table 3 ). The mean (SD) number of inpatient admissions per patient per month among patients with ≥1 inpatient admission was 2.2 (1.5) during the 12-month baseline and 3.2 (2.7) during the follow-up period. Among patients with ≥1 inpatient admission, the median (IQR) length of stay was 5.0 days (3.0–10.1 days) in the baseline, and 5.6 days (3.0–10.3 days) in the follow-up period per patient per admission ( Table 3 ). Table 3. All-cause healthcare resource utilization. Health care resource utilization 12-month baseline ≥ 6 months follow-up a Number of unique patients, n 228 228 Patients with inpatient admissions, n (%) 110 (48.3%) 162 (71.1%) Number of inpatient admissions b Mean (SD) 2.2 (1.5) 3.2 (2.7) Median (IQR) 2.0 (1.0–3.0) 2.0 (1.0–4.0) Length of inpatient stay c , days Mean (SD) 8.1 (8.2) 8.1 (7.7) Median (IQR) 5.0 (3.0–10.1) 5.6 (3.0–10.3) Patients with Emergency Department (ED) d visits, n (%) 65 (28.5%) 107 (46.9%) Patients with outpatient visits, n (%) 228 (100%) 227 (99.6%) Number of outpatient visits, per patient per month Mean (SD) 9.6 (5.1) 11.2 (6.5) Median (IQR) 8.8 (6.1–11.8) 9.8 (7.1–13.9) Patients with physician office visits, n (%) 228 (100%) 225 (98.7%) Number of physician office visits, per patient per month Mean (SD) 2.5 (1.3) 2.8 (2.0) Median (IQR) 2.2 (1.5–3.2) 2.3 (1.6–3.4) Patients with Skilled Nursing Facility (SNF) utilization, n (%) ≤5 16 (7.0%) Abbreviations: IQR, interquartile range; SD, standard deviation. a The median duration of follow-up was 11.8 months. b The mean and median number of inpatient admissions during the entire baseline and follow-up periods. c The length of stay during each hospitalization. d Emergency department visits resulting in hospitalization were counted as inpatient hospitalization only. Cells with n ≤5 are not specified to protect member privacy.
Table 3.
All-cause healthcare resource utilization.
Abbreviations: IQR, interquartile range; SD, standard deviation. a The median duration of follow-up was 11.8 months. b The mean and median number of inpatient admissions during the entire baseline and follow-up periods. c The length of stay during each hospitalization. d Emergency department visits resulting in hospitalization were counted as inpatient hospitalization only. Cells with n ≤5 are not specified to protect member privacy.
| Health care resource utilization | 12-month baseline | ≥ 6 months follow-up a |
| Number of unique patients, n | 228 | 228 |
| Patients with inpatient admissions, n (%) | 110 (48.3%) | 162 (71.1%) |
| Number of inpatient admissions b | ||
| Mean (SD) | 2.2 (1.5) | 3.2 (2.7) |
| Median (IQR) | 2.0 (1.0–3.0) | 2.0 (1.0–4.0) |
| Length of inpatient stay c , days | ||
| Mean (SD) | 8.1 (8.2) | 8.1 (7.7) |
| Median (IQR) | 5.0 (3.0–10.1) | 5.6 (3.0–10.3) |
| Patients with Emergency Department (ED) d visits, n (%) | 65 (28.5%) | 107 (46.9%) |
| Patients with outpatient visits, n (%) | 228 (100%) | 227 (99.6%) |
| Number of outpatient visits, per patient per month | ||
| Mean (SD) | 9.6 (5.1) | 11.2 (6.5) |
| Median (IQR) | 8.8 (6.1–11.8) | 9.8 (7.1–13.9) |
| Patients with physician office visits, n (%) | 228 (100%) | 225 (98.7%) |
| Number of physician office visits, per patient per month | ||
| Mean (SD) | 2.5 (1.3) | 2.8 (2.0) |
| Median (IQR) | 2.2 (1.5–3.2) | 2.3 (1.6–3.4) |
| Patients with Skilled Nursing Facility (SNF) utilization, n (%) | ≤5 | 16 (7.0%) |
At baseline, 28.5% of patients utilized ED visits which increased during follow-up to 46.9%. The mean number of visits was 1.4 (0.7) at baseline and 1.7 (1.2) during follow-up ( Table 3 ). All patients (100%) had at least one outpatient and physician office visit at baseline. The mean (SD) number of outpatient visits PPPM was 9.6 (5.1) in the baseline and 11.2 (6.5) in the follow-up period. The mean (SD) number of physician office visits PPPM was 2.5 (1.3) in the baseline and 2.8 (2.0) in the follow-up period ( Table 3 ).
Healthcare costs
All-cause PPPM total healthcare costs (medical + pharmacy) inflated to 2022 US dollars increased during the follow-up period, with mean (SD) total costs of $41,358 ($33,703) compared to $35,964 ($31,977) at baseline. Inpatient utilization accounted for most of the increases observed during follow-up, with mean (SD) PPPM inpatient cost of $11,746 ($21,043) compared to $4,591 ($10,869) at baseline ( Table 4 ). Median (IQR) all-cause total outpatient costs at baseline were $19,339 ($10,490–$31,258) and somewhat similar to $19,244 ($10,570 - $32,987) in the follow-up. Nonetheless, median (IQR) all-cause total medical costs were $23,705 ($12,872 - $35,607) at baseline and increased to $29,153 ($15,467 - $48,909) during follow-up. Median (IQR) all-cause total costs were $27,921 ($15,420 - $45,318) at baseline and $32,463 ($19,689 - $54,425) at follow-up. Table 4. All-cause healthcare costs per patient per month (PPPM) in 2022 USD ($). Healthcare Costs, PPPM a 12-month baseline ≥6 months follow-up b Number of unique patients, n 228 228 Total medical c costs, Mean (SD), $ 31,650 (31,844) 37,618 (33,448) Total inpatient costs, Mean (SD), $ 4,591 (10,869) 11,746 (21,043) Total ED costs, Mean (SD), $ 143 (370) 496 (1,788) Total outpatient costs, Mean (SD), $ 26,903 (28,194) 25,200 (23,427) Total skilled nursing facility costs, Mean (SD), $ 13 (119) 176 (855) Total pharmacy costs, Mean (SD), $ 4,314 (7,454) 3,740 (7,237) Total costs (medical + pharmacy), Mean (SD), $ 35,964 (31,977) 41,358 (33,703) Abbreviations: PPPM, per patient per month; SD, standard deviation; ED, emergency department. a Costs were a sum of plan paid, and patient paid amounts, where patient paid costs include all coinsurance, deductible, and co-payment. Costs were adjusted to 2022 dollars based on updated Consumer Price Index (CPI) information from the US Bureau of Labor Statistics. b The median duration of follow-up was 11.8 months. c Total medical costs (sum of inpatient, outpatient, ED, and skilled nursing facility costs).
Table 4.
All-cause healthcare costs per patient per month (PPPM) in 2022 USD ($).
Abbreviations: PPPM, per patient per month; SD, standard deviation; ED, emergency department. a Costs were a sum of plan paid, and patient paid amounts, where patient paid costs include all coinsurance, deductible, and co-payment. Costs were adjusted to 2022 dollars based on updated Consumer Price Index (CPI) information from the US Bureau of Labor Statistics. b The median duration of follow-up was 11.8 months. c Total medical costs (sum of inpatient, outpatient, ED, and skilled nursing facility costs).
| Healthcare Costs, PPPM a | 12-month baseline | ≥6 months follow-up b |
| Number of unique patients, n | 228 | 228 |
| Total medical c costs, Mean (SD), $ | 31,650 (31,844) | 37,618 (33,448) |
| Total inpatient costs, Mean (SD), $ | 4,591 (10,869) | 11,746 (21,043) |
| Total ED costs, Mean (SD), $ | 143 (370) | 496 (1,788) |
| Total outpatient costs, Mean (SD), $ | 26,903 (28,194) | 25,200 (23,427) |
| Total skilled nursing facility costs, Mean (SD), $ | 13 (119) | 176 (855) |
| Total pharmacy costs, Mean (SD), $ | 4,314 (7,454) | 3,740 (7,237) |
| Total costs (medical + pharmacy), Mean (SD), $ | 35,964 (31,977) | 41,358 (33,703) |
Discussion
In this retrospective observational study using real-world data from the HIRD®, a large US-based integrated data source, the clinical and economic burden of RRMM was evaluated in patients who were eligible for but had not yet received novel therapies like BCMA-targeted CAR-T or bispecifics. In the present study, MM-related conditions and complications present a significant clinical burden. Anemia was one of the most prevalent MM-related complications. Compared to previous studies in which the prevalence of anemia ranged from 68.8% to 72.0% in the first three LOTs, our finding of 65.4% was in line with other published work [ 31 , 32 ]. In our population, hypercalcemia was present in 8.8% of patients, which is comparable to other real-world studies conducted in the US [ 31 , 32 ]. In the present study, 32.9% of the population had existing bone disease at baseline. Beyond causing morbidity, bone disease in MM can lead to skeletal related events, which have been determined to be a significant risk factor for overall survival (HR: 4.03, 95% CI 1.97–8.24, p < 0.001) [ 32 ]. Renal dysfunction, an indicator of MM effect and toxicities from prior treatments, was present in 36.8% of the current study sample at baseline, which is comparable to 31.8% previously reported in a different real-world study [ 31 ]. The prevalence of MM-related conditions and complications, recalled by the acronym “CRAB” (hypercalcemia, renal insufficiency, anemia, and bone lytic lesions), is well established, and the presence of any of these conditions qualifies as a myeloma defining event for diagnosis [ 32 , 33 ]. Treating MM often leads to overall clinical improvement; thus, monitoring for the presence of MM-related conditions and complications can indicate when adjustments to the treatment plan may be necessary.
Furthermore, neutropenia, peripheral neuropathy, and hypogammaglobulinemia, secondary to MM disease processes and treatment-related factors were common within the sample. The presence of these conditions indicates the risk of additional burden of illness due to infection and quality of life issues, which may require intervention such as dosage adjustments, prophylactic vaccination, antibiotics, or treatment with intravenous immune globulins [ 3 , 33–35 ]. Comorbidities were highly prevalent in the present study with a mean QCI of 2.8. This is noteworthy, as Sverrisdottir et al. reported that patients with MM and comorbidities at diagnosis were at a higher risk of death compared to those without comorbidities, and the risk increased incrementally with the number of comorbidities [ 36 ].
Clinical outcomes in the present study revealed that treatment failure rates (44.7% within 6 months), TTNT, and death rates were similar to those previously reported in studies that were conducted prior to the approval of BCMA-targeted CAR-T and bispecific therapies [ 37–39 ]. In a study by Madduri et al., among TCE patients initiating a new LOT (indicating treatment failure) between 1995 and 2018, the median time to new treatment was 6.2 months [ 37 ]. In a slightly older Medicare TCE population, (mean age 73.9 years, (SD) 5.2) observed between 2016 and 2019, Hlavacek et al. found the mean (SD) time to new treatment was 5.9 (5.7) months, median was 4.4 months, and 19.7% of patients ( n = 330 of 1672) died within 6 months [ 38 ]. In a study conducted in France, including some who became TCE during the study (2013 – 2019), Leleu et al. found that among the 578 patients who had a next LOT after becoming TCE, the 6-month survival for patients receiving any subsequent treatment (35% death rate) was better than those who received no subsequent treatment (87% death rate) [ 39 ]. In Leleu’s population, the overall 6-month death rate was 39% for patients with at least five LOTs and who received a subsequent LOT after TCE [ 39 ]. By contrast, studies of BCMA-targeted CAR-T and bispecific therapies have demonstrated more optimistic findings. For instance, Idecabtagene autoleucel treatment among RRMM TCE patients with ≥3 previous LOT resulted in a 73% response rate (33% complete response) with a median progression-free survival of 8.8 months (95% CI: 5.6 to 11.6) [ 40 ]. Ciltacabtagene autoleucel, evaluated in patients with RRMM, ≥3 LOT, and TCE, achieved a stringent complete response among 82.5% (95% CI: 73.4 to 89.4) and had not reached median duration of response, progression-free survival, or overall survival at a median 27.7 months of follow-up [ 41 ]. Teclistamab, evaluated in a longer-term analysis (30.4 months) among patients with RRMM, TCE, and ≥3 LOT, resulted in an overall response rate of 63.0% (complete response rate 45.5%), median progression-free survival of 11.4 months, median duration of response of 21.6 months, and median overall survival of 21.9 months [ 42 , 43 ]. Elranatamab treatment among patients with TCE RRMM with a median follow-up of 28.4 months (95% CI: 28.0 to 29.0) resulted in an overall response rate of 61.0% (37.4% complete response rate), median progression-free survival of 17.2 months (95% CI: 9.8-not-estimable), median duration of response not yet reached, and a median overall survival of 24.6 months (95% CI: 13.4-not-estimable) [ 44 ]. Talquetamab, in a phase 1/2 study of patients with TCE RRMM and ≥3 previous LOTs, resulted in an overall response rate of 72%, median duration of response 9.3 months (range: 1–23+), and a median progression-free survival of 7.5 months (95% CI: 5.7 to 9.2) [ 45 , 46 ]. The findings of the present study confirm that patients who have RRMM experience treatment failure, have poor outcomes, and need access to more effective treatment options. With the recent FDA approval for BCMA-targeted therapies in earlier LOTs, the availability of these therapies in earlier lines provides more options for patients with TCE RRMM who may not respond well to traditional treatments.
Beyond the clinical burden, the mean annual all-cause healthcare costs of heavily pretreated RRMM patients present a significant economic burden to healthcare systems [ 47 ]. Prior studies evaluating costs among patients who experienced disease progression during the first, second, and third LOTs reported mean total annual all-cause healthcare costs as $229,073, $311,918, and $341,503, respectively [ 47–49 ]. In our population, the overall utilization and costs of healthcare services during the follow-up period showed impactful increases from baseline to follow-up in the number of inpatient admissions, ED visits, and SNF stays. Most of the increased costs observed during follow-up were associated with inpatient visits, indicating possible disease or treatment complications. Other healthcare categories (outpatient and pharmacy) remained consistent with baseline utilization frequency and associated costs as patients continued to be treated with therapies from the same classes that they had previously used. The baseline total all-cause costs PPPM in the present study were somewhat similar to previously reported costs (mean $37,033, 95%CI $33,454–$40,612) among TCE patients with a mean of 3.6 prior LOTs, adjusted to 2020 US dollars [ 37 ]. In the present study with a median follow-up duration of 11.8 months, mean total all-cause costs PPPM were $41,358, with outpatient costs contributing the majority of the overall expense, consistent with findings from other published healthcare costs associated with heavily pre-treated patients with RRMM [ 37 , 47–49 ].
Study limitations
While claims data are valuable for the efficient and practical examination of healthcare outcomes, treatment patterns, resource utilization, and costs, they are collected for payment and not for research purposes. Therefore, there are limitations associated with the use of claims data, including errors in coding, codes included as rule-out criterion, and limited reporting on other patient characteristics such as clinical symptoms, and smoking status. The identified LOT represents the best estimates using claims data and should be interpreted cautiously. Finally, the study population was queried from US commercially insured and Medicare Advantage members and those with available clinical data, whereby limiting the generalizability of these results to other population segments, such as traditional fee-for-service Medicare, Medicaid, and uninsured patients.
Future research
While our study provides real-world evidence of the clinical and economic burden for patients with RRMM who are eligible for novel BCMA-targeted CAR-T and bispecific therapies, further research is needed to assess the impact of early initiation on these outcomes. To date, there is limited published evidence that measures the economic impact of novel therapies like BCMA- and GPRC5D-targeted treatment. The findings of this study pave the way for future research by offering a current and realistic assessment of RRMM therapies.
Conclusions
RRMM patients who are TCE, have received ≥4 LOTs, and are eligible for BCMA-targeted therapies but have not yet received them face significant comorbidities and MM-related conditions, complications, poor treatment outcomes, and a severe economic burden, especially after starting fifth or later LOTs. This population of patients endured another LOT after becoming eligible. As MM is a rare and chronic disorder, the findings of this study could guide future research in establishing more baseline patient assessments to minimize confounding biases and support care management to improve patient outcomes. Furthermore, the results from this study could support economic assessment and value framework for novel therapies that may improve treatment outcomes for patients with heavily pretreated RRMM.
Acknowledgments
We acknowledge Carelon Research employees, Ms Elizabeth Marks, who provided medical writing support, and Mr Gregory Parent for project management, both of which were funded by Janssen Scientific Affairs, LLC.
Article highlights
This study examined the clinical and economic burden of heavily pre-treated patients diagnosed with relapsed or refractory multiple myeloma (RRMM). The patients were eligible for but had not received the newly approved B-cell maturation antigen (BCMA)-targeted chimeric antigen receptor (CAR) T-cell and/or bispecific therapies. We used de-identified data from Carelon Research’s Healthcare Integrated Research Database (HIRD®), including payer claims and clinical data from the Cancer Care Quality Program (CCQP). Patients who would have met the eligibility for BCMA-targeted therapies per label and started a new line of therapy were identified during the patient identification period from January 1, 2016, to September 30, 2022. Outcomes of interest were comorbidities, disease complications, treatment failure rates, mortality rates, healthcare resource utilization, and costs to ascertain the disease burden. The study population included 228 patients with a median age of 64 years; comorbidities (e.g., hypertension) and MM-related conditions and complications (e.g., anemia, neutropenia, and renal disease) were common at baseline. Healthcare resource utilization and costs, particularly inpatient visits, surged for heavily pre-treated patients with RRMM after initiating their fifth line of therapy or later, with a striking all-cause total monthly cost of $41,358 per patient. Patients with heavily pre-treated RRMM endure significant clinical and economic burdens, warranting exploration of novel treatments. The research underscores the need for more studies centering on the clinical, and economic outcomes of novel treatments like BCMA-targeted therapies, especially when initiated early after becoming eligible.
Author contributions
DL, NK, JF, and BW contributed to the concept design. All authors contributed to the study design, interpretation, writing, and approval of the manuscript. RWD, JB, HT, CN, FA, and SV contributed to study conduct and data analysis. All the authors have met criteria for authorship as recommended by the International Committee of Medical Journal Editors (ICMJE).
Disclosure statement
DL, NK, JF, and BW are employees of Johnson & Johnson and may hold stocks or stock options of Johnson & Johnson. EEM is a research contractor of Johnson & Johnson. RD, JB, HT, and SV are employees of Carelon Research, a consultancy whose activities on research projects are funded by various life sciences companies and health plans. CN was an employee of Carelon Research during the conduct of the study. FA is an employee of Panalgo. SG is an employee of the University of Alabama at Birmingham, which receives research funding from CareVive Systems, PackHealth, Sanofi, and Janssen Oncology. SG also receives honoraria from CareVive, OncLive, and Sanofi. The authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed.
Medical Writing support was provided by Elizabeth Marks from Carelon Research and was funded by Janssen Scientific Affairs, LLC.
Ethical declaration
All data used in this study were accessed in a fully Health Insurance Portability and Accountability Act compliant manner. As this research was conducted on fully de-identified data, it did not qualify as human subject research as defined in 45 CFR 45.102(f)(2). As such, this study was exempt from institutional review board approval, and therefore the Helsinki Declaration does not apply; however, the principals of the Helsinki Declaration (which are “fundamental to respect for and protection of all research participants”) are important to our team and organization and informed our research processes.
Data availability statement
Due to contractual obligations to the data sources, supporting data are not available.
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